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Amplicon Sequencing using the Long-Read Sequencing Technologies
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Association mapping from sequencing reads using k-mers.

Atif Rahman1, Ingileif Hallgrímsdóttir2, Michael Eisen3,4

  • 1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, Berkeley, United States.

Elife
|June 14, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a novel alignment-free method for genome-wide association studies (GWAS) using k-mers from whole-genome sequencing data. This approach identifies genetic associations, including structural variations, beyond the reference genome.

Keywords:
E. coliassociation mappingcardiovascular diseasesepidemiologygeneticsgenomicsglobal healthhumank-mersreference freesequencing

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Area of Science:

  • Genomics
  • Bioinformatics
  • Population Genetics

Background:

  • Genome-wide association studies (GWAS) traditionally use microarrays or read mapping to reference genomes.
  • Reference genome limitations restrict association studies to known sequences and preclude novel variant discovery.

Purpose of the Study:

  • To develop and validate an alignment-free k-mer counting method for association studies using whole-genome sequencing data.
  • To identify genetic associations, including those with structural variations and non-reference sequences.
  • To infer population stratification from k-mer data.

Main Methods:

  • An alignment-free method based on counting k-mers in whole-genome sequencing reads.
  • Direct association testing between k-mers and categorical phenotypes.
  • Local assembly of significant k-mers to identify sequence differences.
  • Analysis of 1000 Genomes data and an E. coli ampicillin resistance dataset.

Main Results:

  • The k-mer method largely agrees with standard GWAS approaches on the 1000 Genomes data.
  • The method successfully identifies associations with structural variations and sequences absent from the reference genome.
  • Population stratification can be inferred using k-mer frequencies.
  • Validation in an E. coli dataset demonstrates efficacy for trait association.

Conclusions:

  • The alignment-free k-mer approach offers a powerful alternative for GWAS, expanding discovery beyond reference genomes.
  • This method enhances the detection of structural variations and novel genetic associations.
  • K-mer analysis provides insights into population structure and trait associations.